CRISP uses the observed stability of positive voxel probability rankings under domain shift to build and iteratively refine high-precision and high-recall priors via latent feature perturbation, enabling parameter-free robust segmentation.
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Evaluating prediction-time batch normalization for robustness under covariate shift
14 Pith papers cite this work, alongside 95 external citations. Polarity classification is still indexing.
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T-VSS is a lightweight test-time defense that steers attacked visual features in VLMs using sample-specific low-rank subspaces and reliability-weighted entropy minimization to improve robustness.
A framework to identify and convert foldable layer normalizations to RMSNorm for exact equivalence and faster inference in deep neural networks.
Lens adapts camera sensors in real time via the VisiT confidence-based quality indicator to improve vision model accuracy on domain-shifted images, shown on ImageNet-ES and a new diverse benchmark.
A multi-level diversification wrapper for test-time adaptation that treats entropy minimization as multi-hypothesis inference to reduce underspecification and improve robustness by 1-4%.
TopoTTA integrates persistent homology into test-time adaptation to derive topological pseudo-labels from anomaly maps, improving segmentation by an average 15% F1 on six benchmarks while generalizing across 2D and 3D data.
Entropy minimization amplifies prediction bias from merged feature clusters under distribution shifts, and DSBR mitigates collapse by equalizing predicted class contributions to the unsupervised loss.
QTAML derives WKB-based tunneling noise models for AI weights with affine mean drift and per-bit variance hierarchy, then uses them in TAC to achieve 95% clean accuracy with 3.4-33.6x less ECC overhead than baselines on CNNs and transformers.
Diversity-aware memory policies improve test-time adaptation performance most under constrained memory budgets and challenging non-i.i.d. streams.
TAME uses a Mixture-of-Experts prompt bank with input-dependent routing and three unsupervised objectives to adaptively defend CLIP against adversarial attacks at inference time, achieving at least 49.1% robustness gain on 11 datasets.
Deployment-time Shortcut Guardrail uses unsupervised gradient attribution on a converged text encoder alone to mitigate shortcut learning and match training-time baselines under distribution shift.
Test-time entropy minimization adapts models by optimizing for confident predictions, reducing error on corrupted ImageNet-C and enabling source-free domain adaptation.
Threshold Modulation dynamically adjusts firing thresholds in SNNs via neuronal dynamics-inspired normalization to enable online test-time adaptation under distribution shifts.
GMN4AD applies graph matching and test-time contrastive adaptation to improve Alzheimer's diagnosis accuracy on heterogeneous multi-center sMRI datasets.
citing papers explorer
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CRISP: Rank-Guided Iterative Squeezing for Robust Medical Image Segmentation under Domain Shift
CRISP uses the observed stability of positive voxel probability rankings under domain shift to build and iteratively refine high-precision and high-recall priors via latent feature perturbation, enabling parameter-free robust segmentation.
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T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models
T-VSS is a lightweight test-time defense that steers attacked visual features in VLMs using sample-specific low-rank subspaces and reliability-weighted entropy minimization to improve robustness.
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Enjoy Your Layer Normalization with the Computational Efficiency of RMSNorm
A framework to identify and convert foldable layer normalizations to RMSNorm for exact equivalence and faster inference in deep neural networks.
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Adaptive Camera Sensor for Vision Models
Lens adapts camera sensors in real time via the VisiT confidence-based quality indicator to improve vision model accuracy on domain-shifted images, shown on ImageNet-ES and a new diverse benchmark.
-
Multi-Hypothesis Test-Time Adaptation to Mitigate Underspecification
A multi-level diversification wrapper for test-time adaptation that treats entropy minimization as multi-hypothesis inference to reduce underspecification and improve robustness by 1-4%.
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Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation
TopoTTA integrates persistent homology into test-time adaptation to derive topological pseudo-labels from anomaly maps, improving segmentation by an average 15% F1 on six benchmarks while generalizing across 2D and 3D data.
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Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imaging
Entropy minimization amplifies prediction bias from merged feature clusters under distribution shifts, and DSBR mitigates collapse by equalizing predicted class contributions to the unsupervised loss.
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Quantum Tunneling-Aware Machine Learning: Physics-Derived Noise Models for Robust Deployment
QTAML derives WKB-based tunneling noise models for AI weights with affine mean drift and per-bit variance hierarchy, then uses them in TAC to achieve 95% clean accuracy with 3.4-33.6x less ECC overhead than baselines on CNNs and transformers.
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GoTTA be Diverse: Rethinking Memory Policies for Test-Time Adaptation
Diversity-aware memory policies improve test-time adaptation performance most under constrained memory budgets and challenging non-i.i.d. streams.
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TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models
TAME uses a Mixture-of-Experts prompt bank with input-dependent routing and three unsupervised objectives to adaptively defend CLIP against adversarial attacks at inference time, achieving at least 49.1% robustness gain on 11 datasets.
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Models Know Their Shortcuts: Deployment-Time Shortcut Mitigation
Deployment-time Shortcut Guardrail uses unsupervised gradient attribution on a converged text encoder alone to mitigate shortcut learning and match training-time baselines under distribution shift.
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Tent: Fully Test-time Adaptation by Entropy Minimization
Test-time entropy minimization adapts models by optimizing for confident predictions, reducing error on corrupted ImageNet-C and enabling source-free domain adaptation.
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Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks
Threshold Modulation dynamically adjusts firing thresholds in SNNs via neuronal dynamics-inspired normalization to enable online test-time adaptation under distribution shifts.
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GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging
GMN4AD applies graph matching and test-time contrastive adaptation to improve Alzheimer's diagnosis accuracy on heterogeneous multi-center sMRI datasets.